Showing papers in "International Journal of Forecasting in 2011"

TL;DR: In this paper, the authors examined the ability of online search intensity to forecast abnormal stock returns and trading volumes and found that search intensity reliably predicts abnormal stock return and trading volume, and that the sensitivity of returns to search intensity is positively related to the difficulty of a stock being arbitraged.

TL;DR: In this paper, the authors derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators.

TL;DR: The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.

TL;DR: In this article, the authors evaluated the performance of various methods for forecasting tourism data and found that pure time series approaches provided more accurate forecasts for tourism data than models with explanatory variables.

TL;DR: In this article, the authors refer to the generalized piecewise linear (GPL) loss function, which nests the asymmetric piece-wise linear loss, and show that the level of the quantile depends on a generic asymmetry parameter which reflects the possibly distinct costs of underprediction and overprediction.

TL;DR: While there were no statistically significant differences in accuracy between the four methods overall, the results differed somewhat at the individual question level, and prediction market participants were least satisfied with the group process and perceived their method as the most difficult.

TL;DR: In this paper, the authors compared the performance of mixed-data sampling and mixed-frequency VAR (MF-VAR) approaches to model specification in the presence of mixed frequency data, e.g. monthly and quarterly series.

TL;DR: In this paper, the authors present a survey of approaches to automate the dating of business cycle turning points on a real time, out-of-sample basis, due to factors such as data revisions and changes in economic relationships over time.

TL;DR: The forecasting model with which the author participated in the NN5 forecasting competition is introduced, to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature.

TL;DR: In this paper, the authors consider the idea of diversity being accomplished by using different time aggregations and show that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.

TL;DR: The authors analyzed the accuracy, unbiasedness and efficiency of professional macroeconomic forecasts and found that the forecasts tend to be biased in situations where the forecasters have to learn about large structural shocks or gradual changes in the trend of a variable.

TL;DR: Based on the structural time series model (STSM) and the time-varying parameter (TVP) regression approach, the authors developed the causal STSM further by introducing TVP estimation of the explanatory variable coefficients, and therefore combined the merits of the STSM and TVP models.

TL;DR: The results suggest that the new forecasting method can perform well relative to four other standard statistical techniques from the literature, namely the ARIMA, Theta, Holt-Winters' and Holt's Damped Trend methods.

TL;DR: This paper introduces three approaches to forecasting interval-valued time series based on multilayer perceptron (MLP) neural networks and Holt’s exponential smoothing methods, respectively.

TL;DR: In this paper, the predictive ability of the binary dependent dynamic probit model in predicting the direction of monthly excess stock returns was investigated, and it was shown that the predictive power of the model for a binary recession indicator appears to be the most useful predictive variable, and once employed, the sign of the excess return is predictable in-sample.

TL;DR: A heuristic method for setting both the σ parameter of the Gaussian kernel and the regularization hyperparameter based on information extracted from the time series to be modelled is presented and evaluated.

TL;DR: The potential of group (vs. individual) forecasting from the perspective of the social psychology of groups is analyzed in this article, and several simulations are presented to demonstrate the dependence of group aggregation accuracy upon factors such as group size, the accuracy and distribution of individual forecasts, and shared representations of the forecasting problem.

TL;DR: A model for forecasting match results for the top tier of men’s professional tennis, the ATP tour, is introduced and provides superior forecasts according to each of five criteria measuring the predictive performance, two of which relate to betting returns.

TL;DR: The authors examined combining exponential smoothing point and interval forecasts using weights derived from AIC, small-sample-corrected AIC and BIC on the M1 and M3 Competition datasets.

TL;DR: In this article, the authors present the Delphi method, in its Policy Delphi variant, as an efficient mechanism for carrying out consultations regarding regulatory actions that affect professional bureaucracies, and also, in the last analysis, for forecasting and constructing their future.

TL;DR: In this paper, a number of approaches to forecasting short-to medium-term air traffic flows are examined, including pooled ADL models and the enhanced models with the addition of a "world trade" variable.

TL;DR: This article found that participants often ignored advice when revising an estimate but averaged estimates when combining, despite receiving identical feedback about the accuracy of past judgments, and they compared two prominent explanations for this, differential access to reasons and egocentric beliefs, and found that neither adequately accounts for the overweighting of the self.

TL;DR: In this article, the authors assess the performance of alternative procedures for forecasting the daily volatility of the euro's bilateral exchange rates using 15 min data using realized volatility and traditional time series volatility models.

TL;DR: A multi-output extension of conventional local modeling approaches is discussed, and three distinct criteria for performing conditionally dependent model selection are presented and compared.

TL;DR: In this paper, the authors used susceptibility to the framing effect as a measure of decision quality and found that the advantage of groups over individuals in decision-making depends on the group composition.

TL;DR: This paper considers several methods of producing a single forecast from several individual ones, including principal components, dynamic factor models, partial least squares and sliced inverse regression.

TL;DR: In this article, the authors used large factor models (FMs), which accommodate a large cross-section of macroeconomic time series for forecasting the per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy.

TL;DR: In this paper, a new method for forecasting the trend of time series, based on mixture of MLP experts, is presented, and three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange.

TL;DR: In this paper, the authors analyse the density forecasts of UK inflation obtained from the Bank of England's Survey of External Forecasters, considering both the survey average forecasts published in the Bank's quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank.

TL;DR: In this paper, different methods for combining probability forecasts are considered, and the properties of various combination schemes for a number of plausible data generating processes, and indicate which types of combinations are likely to be useful.